Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia

Journal of Earth system science

Saro Lee1¹  Biswajeet Pradhan2² 

[1] Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM) 30, Kajung-Dong, Yusung-Gu, Daejeon, Korea.$$;Cilix Corporation, Lot L4-I-6, Level 4, Enterprise 4, Technology Park Malaysia, Bukit Jalil Highway, Bukit Jalil, 57000, Kuala Lumpur, Malaysia.$$

关键词: Landslide;    frequency ratio;    landslide hazard;    risk analysis;    geographic information system;    remote sensing.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/ geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability–frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

【 授权许可】

Unknown